refactor(dify): Optimize the Dify API output parsing and workflow processing logic (#2027)

- Add the _extract_dify_text_output method to uniformly handle the parsing of Dify output content

- Modify the content extraction method for the answer node in workflow mode

- Add workflow mode detection logic to support the workflow_started event

- Handle error state checks upon completion of the workflow

- Improve the message chunking logic for both basic and workflow modes

- Add a mechanism to capture answer content upon completion of a workflow node

src/langbot/pkg/provider/runners/difysvapi.py

@@ -72,6 +72,28 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                 content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
             return content, thinking_content
 
+    def _extract_dify_text_output(self, value: typing.Any) -> str:
+        """Extract text content from Dify output payload."""
+        if value is None:
+            return ''
+        if isinstance(value, dict):
+            content = value.get('content')
+            if isinstance(content, str):
+                return content
+            return json.dumps(value, ensure_ascii=False)
+        if isinstance(value, str):
+            text = value.strip()
+            if not text:
+                return ''
+            try:
+                parsed = json.loads(text)
+            except json.JSONDecodeError:
+                return value
+            if isinstance(parsed, dict) and isinstance(parsed.get('content'), str):
+                return parsed['content']
+            return value
+        return str(value)
+
     async def _preprocess_user_message(self, query: pipeline_query.Query) -> tuple[str, list[dict]]:
         """预处理用户消息，提取纯文本，并将图片/文件上传到 Dify 服务
 
@@ -192,7 +214,8 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             if mode == 'workflow':
                 if chunk['event'] == 'node_finished':
                     if chunk['data']['node_type'] == 'answer':
-                        content, _ = self._process_thinking_content(chunk['data']['outputs']['answer'])
+                        answer = self._extract_dify_text_output(chunk['data']['outputs'].get('answer'))
+                        content, _ = self._process_thinking_content(answer)
 
                         yield provider_message.Message(
                             role='assistant',
@@ -405,6 +428,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             for f in upload_files
         ]
 
+        mode = 'basic'
         basic_mode_pending_chunk = ''
 
         inputs = {}
@@ -430,11 +454,12 @@ class DifyServiceAPIRunner(runner.RequestRunner):
         ):
             self.ap.logger.debug('dify-chat-chunk: ' + str(chunk))
 
-            # if chunk['event'] == 'workflow_started':
+            if chunk['event'] == 'workflow_started':
-            #     mode = 'workflow'
+                mode = 'workflow'
-            # if mode == 'workflow':
+            elif chunk['event'] in ('node_started', 'node_finished', 'workflow_finished'):
-            # elif mode == 'basic':
+                # Some Dify deployments may omit workflow_started in streamed chunks.
-            # 因为都只是返回的 message也没有工具调用什么的，暂时不分类
+                mode = 'workflow'
+
             if chunk['event'] == 'message':
                 message_idx += 1
                 if remove_think:
@@ -457,8 +482,18 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
             if chunk['event'] == 'message_end':
                 is_final = True
+            elif chunk['event'] == 'workflow_finished':
+                is_final = True
+                if chunk['data'].get('error'):
+                    raise errors.DifyAPIError(chunk['data']['error'])
 
-            if is_final or message_idx % 8 == 0:
+            if mode == 'workflow' and chunk['event'] == 'node_finished':
+                if chunk['data'].get('node_type') == 'answer':
+                    answer = self._extract_dify_text_output(chunk['data'].get('outputs', {}).get('answer'))
+                    if answer:
+                        basic_mode_pending_chunk = answer
+
+            if (is_final or message_idx % 8 == 0) and (basic_mode_pending_chunk != '' or is_final):
                 # content, _ = self._process_thinking_content(basic_mode_pending_chunk)
                 yield provider_message.MessageChunk(
                     role='assistant',